/**
 * Representation of the 'AzureOpenAiChatCompletionsRequestCommon' schema.
 */
export type AzureOpenAiChatCompletionsRequestCommon = {
    /**
     * What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
     * We generally recommend altering this or `top_p` but not both.
     * @example 1
     * Default: 1.
     * Maximum: 2.
     */
    temperature?: number | null;
    /**
     * An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.
     * We generally recommend altering this or `temperature` but not both.
     * @example 1
     * Default: 1.
     * Maximum: 1.
     */
    top_p?: number | null;
    /**
     * If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a `data: [DONE]` message.
     */
    stream?: boolean | null;
    /**
     * Up to 4 sequences where the API will stop generating further tokens.
     */
    stop?: string | string[];
    /**
     * The maximum number of tokens allowed for the generated answer. By default, the number of tokens the model can return will be (4096 - prompt tokens). This value is now deprecated in favor of `max_completion_tokens`, and is not compatible with o1 series models.
     * Default: 4096.
     */
    max_tokens?: number;
    /**
     * An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.
     */
    max_completion_tokens?: number | null;
    /**
     * Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
     * Maximum: 2.
     * Minimum: -2.
     */
    presence_penalty?: number;
    /**
     * Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
     * Maximum: 2.
     * Minimum: -2.
     */
    frequency_penalty?: number;
    /**
     * Modify the likelihood of specified tokens appearing in the completion. Accepts a json object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
     */
    logit_bias?: Record<string, any> | null;
    /**
     * A unique identifier representing your end-user, which can help Azure OpenAI to monitor and detect abuse.
     * @example "user-1234"
     */
    user?: string;
} & Record<string, any>;
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